Want to learn more about Labelbox?

Labelbox’s fully configurable platform enables teams to create and manage ML training data as quickly as possible.

Labelbox provides a suite of collaboration, analytics, and labeling automation tools to help keep your training data costs down while making the process repeatable, predictable, and less painful. 

Training Data Platforms 101

Data science teams spend a disproportionate amount of their time processing, labeling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.

TRAINING DATA MANAGEMENT

Training Data Platforms 101

Data science teams spend a disproportionate amount of their time processing, labeling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.

TRAINING DATA MANAGEMENT

LABELING AUTOMATION

Guide to Labeling Automation

Data science teams spend a disproportionate amount of their time processing, labeling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.

Guide to Labeling Automation

Data science teams spend a disproportionate amount of their time processing, labeling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.

LABELING AUTOMATION

ML Unboxed: How to diagnose and improve model performance

Welcome to Labelbox's new webinar series, ML Unboxed. Geared towards ML practitioners, you'll learn essential ML best practices and concepts, coupled with hands-on workshops on using the Labelbox platform.

In this webinar, product manager Gareth Jones will take you through a hands-on tutorial to teach you how to diagnose model errors and prioritize data that will dramatically improve performance. In a recent blog post, we discussed best practices to help teams spend less time labeling data that doesn’t lead to meaningful improvements in performance. 

Gareth walks through specific workflows to help increase your iteration velocity and improve performance faster, including how to: 

• Use an open-source model or the model of your choice for transfer learning (we’ll use the Detectron2 Panoptic Segmentation model)
• Diagnose initial model performance with Model Diagnostics to understand where to focus your first batch of labeling
• Pre-label data with Similarity Search and labeling functions
• Prioritize high-value data for labeling based on model performance 
• Retrain your model with a new batch of ground truth labels and compare its performance with the previous version 

With this end-to-end workflow you’ll be able to iterate faster and measurably improve your model’s performance while labeling less data.

ML Unboxed: How to diagnose and improve model performance

Building a better AI data engine

AI practitioners regularly face a few common challenges: too much time spent building and maintaining tools and infrastructure, siloed AI development efforts, and fragmented processes to evaluate quality.

Building an effective "data engine" within your organization allows you to improve your model more quickly and reliably. Learn how a training data platform enables you to fuel that engine by harnessing active learning, collaboration, and full transparency into your ML workflow.